Gate-controlled neuromodulatory optical synaptic transistor for adaptive learning and energy-accuracy balance
Abstract
gate bias prior to optical stimulation, effectively mimicking neuromodulatory gain control. Convolutional Neural Network (CNN) training on the CIFAR-10 dataset shows that higher gate biases improve classification accuracy with higher energy use, while weaker biases reduce energy consumption with an adaptive accuracy tradeoff. Our device integrates core synaptic behaviors with gate-controlled gain modulation, effectively emulating neuromodulation and offering a practical and efficient approach to adaptive neuromorphic vision systems.
Fields of Study & Subjects
Advanced Memory and Neural Computing
Neural Networks and Reservoir Computing
Transition Metal Oxide Nanomaterials
Publication details
- Journal / source
- Materials Horizons
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 13
- Issue
- 8
- Pages
- 4032–4042
- Publication date
- 1 January 2026
- Language
- EN